Traditionally, medical image radiology reports are created by transcribing a recording of interpretations of the image data by a radiologist. This results in an unstructured collection of findings subject to the experience and organization of individual radiologists, which varies in syntax and semantics. In contrast, recent studies have shown the advantages of consistent communication and superior evaluation from structured radiology reports. As such there are several ongoing radiology initiatives to standardize templates and lexicography.
Research in natural language processing of medical data have developed models to represent the linguistics and language of reporting by creating a comprehensive interpretation of diagnostic semantics. Recently, work from the National Institute of Heath presented the first study performing a large-scale image/text analysis on a hospital picture archiving and communication system database. While previous studies and proprietary efforts mainly focus on specific domain and ontology to derive text descriptions for report, there is a need for a generic architecture which can accommodate different forms of analytical data as well as being adaptable to multiple clinical domains.